Abstract
Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of regions.
Highlights
Image segmentation is a process that divides an image into different regions
We present our experimental results on segmenting blood cell images based on the selected algorithm such as Mean-shift, Fuzzy c-means, and KMeans
We present three popular methods of clustering algorithm to segment blood cell images
Summary
Segmentation of blood cell images has great potential area helping the expert to diagnose diseases. Segmentation of blood cell images can be seen as a mechanism to assemble area of interest based on certain features such as colour, texture, and shape. Segmentation methods are not suitable for a large amount of data, not efficient and consume time. To overcome this problem, an automated cell segmentation system will be a great tool for researchers and those involved in medical areas. The segmented images will be employed in the process of object recognition and definitely help the experts to recognise the disease quickly. Image segmentation methods can be categorised into five methods; pixel-based [1], region-based [2], edge-based, edge, and region-based hybrid, and clustering based segmentation [3]
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